class_docstr: Base class of stacking classifiers
docstr: Predict target values for X.
  
  Parameters
  ----------
  X : numpy array, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  
  Returns
  ----------
  labels : array-like, shape = [n_samples]
      Predicted class labels.
docstr_tok: ["Predict","target","values","for","X.","Parameters","--","--","--","--","--","X",":","numpy","array",",","shape","=","[","n_samples",",","n_features","]","Training","vectors",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","Returns","--","--","--","--","--","labels",":","array-like",",","shape","=","[","n_samples","]","Predicted","class","labels","."]
code: def predict(self, X):
      """Predict target values for X.
  
      Parameters
      ----------
      X : numpy array, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
  
      Returns
      ----------
      labels : array-like, shape = [n_samples]
          Predicted class labels.
  
      """
      check_is_fitted(self, ["clfs_", "meta_clf_"])
  
      return self._do_predict(X, self.meta_clf_.predict)
  
file_path: mlxtend/mlxtend/classifier/_base_classification.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import ClassifierMixin
  from ..externals.estimator_checks import check_is_fitted
label_desc: Use image node_id 2 for calling the _BaseStackingClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: self